Papers with stance classification tasks
Rumor Detection by Exploiting User Credibility Information, Attention and Multi-task Learning (P19-1)
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| Challenge: | Social media platforms do not always pose authentic information, and rumors spread fear or hate. |
| Approach: | They propose a new multi-task learning approach for rumor detection and stance classification tasks. |
| Outcome: | The proposed model outperforms the state-of-the-art rumor detection approaches on two datasets. |
Tree LSTMs with Convolution Units to Predict Stance and Rumor Veracity in Social Media Conversations (P19-1)
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| Challenge: | Existing approaches to learn from social-media conversations have been proposed to identify and contain fake news shared on social media platforms. |
| Approach: | They propose to represent social-media conversations as binarized constituency trees that allows comparing features in source-posts and their replies effectively. |
| Outcome: | The proposed models outperform the current best model by 12% and 15% on F1-macro for rumor-veracity classification and stance classification tasks respectively. |